National Diabetes Inpatient Audit 2019 Methodology

Published 13 November 2020

## Contents

| Section | Page | |----------------------------------|------| | Introduction | 3 | | Methodology | 3 | | Overview | 3 | | How did we calculate the values in the audit? | 5 | | Statistical glossary | 6 | Introduction

The National Diabetes Inpatient Audit (NaDIA) audit is commissioned by the Healthcare Quality Improvement Partnership (HQIP) as part of the National Clinical Audit and Patient Outcomes Programme (NCAPOP).

The 2019 NaDIA report is the ninth annual snapshot audit of diabetes inpatient care in England and Wales. The audit is open to participation from hospitals with medical, surgical, gynaecology wards or intensive care units.

NaDIA 2019 was a repeat of the 2010 to 2013 and 2015 to 2017 collections. There was no 2014 audit and NaDIA 2018 covered the hospital characteristics only. Wales did not participate in NaDIA 2019, though Welsh organisations have participated in six previous full NaDIA collections: 2011 to 2013 and 2015 to 2017, plus the 2018 hospital characteristics collection.

The audit sets out to measure the quality of diabetes care provided to people with diabetes while they are admitted to hospital, by answering the following questions:

- Did diabetes management minimise the risk of avoidable complications?
- Did harm result from the inpatient stay?
- Was patient experience of the inpatient stay favourable?
- Has the quality of care and patient feedback changed since NaDIA 2010, 2011, 2012, 2013, 2015, 2016 and 2017?

The report will be of interest to the public, especially to people with diabetes. Health planners and policy makers, as well as acute NHS Trusts, Clinical Commissioning Groups (CCGs), Local Health Boards (LHBs), Sustainability and Transformation Partnerships (STPs), Clinical Networks (CNs; formerly Strategic Clinical Networks or SCNs) and other providers and commissioners of specialist diabetes services will also make use of the information in this report.

The report presents findings from the 2019 audit – carried out on a day between 23 and 27 September 2019 – on patients admitted for at least 24 hours to specified types of inpatient ward (see inclusion criteria below). The audit collected data on characteristics of the hospital, patient clinical data and the patient experience using paper-based questionnaires and an electronic data collection tool. Because Wales did not participate in NaDIA 2019, combined England and Wales data from earlier NaDIA collections has been re-analysed to include English NaDIA sites only.

NaDIA is the companion audit to the continuous NaDIA-Harms collection of diabetic inpatient harms in England, which has been undertaken since May 2018. The 2019 NaDIA-Harms report is published here: http://digital.nhs.uk/pubs/nadia-harms2019.

Methodology

Overview

The National Diabetes Inpatient Audit 2019 was carried out by hospital teams in England and Wales on a nominated day between 23 and 27 September 2019. The teams collected patient clinical data and patient experience information, using paper-based questionnaires. Data on characteristics of the hospital including staffing structures was collected using an electronic data collection form submitted to the NHS Digital Strategic Data Collection Service https://datacollection.sdcs.digital.nhs.uk/.

Each participating hospital identified all eligible inpatients with diabetes (see inclusion criteria below) and distributed questionnaires. Where the patient was able and willing, a Patient Experience form was completed alongside a Bedside Audit form providing information on the patient’s medical treatment. If required, the patient was able to complete and return the Patient Experience questionnaire at a later time. The hospital team also completed a Hospital Characteristics questionnaire providing information on the hospital’s resources and staffing structure. Sample copies of the 2019 questionnaires can be found on the NHS Digital website: http://content.digital.nhs.uk/diabetesinpatientaudit.

A patient was included in the inpatient audit if they had been admitted to a bed for 24 hours or more. Patients on an Obstetric or Paediatric ward were excluded from this audit. Mental Health wards were also excluded due to the high prevalence of long stay patients. Other exclusions included:

- Patients who were hyperglycaemic but not yet formally diagnosed with diabetes
- Accident and Emergency
- Day case ward
- Day surgery unit patients
- Observation ward (if patients had been admitted for less than 24 hours)
- Surgical short stay unit (if patients had been admitted for less than 24 hours)
- Palliative care centres
- Community Hospitals.

Once all questionnaires were returned the data was collated and cleaned to produce the analysis for this report.

Where at least one type of questionnaire (either Patient Experience, Bedside Audit or Hospital Characteristics) was returned, the hospital has been counted in the overall participation rate. Hospital Characteristics questionnaires were completed either at hospital level or at site level (i.e. where a number of hospitals were aggregated together e.g. NHS trusts); therefore, prevalence rates are based on the number of participating sites rather than individual hospitals.

All percentages, charts and tables in this report relate to all inpatients in England and Wales, unless otherwise stated.

This differs from the 2011 and 2012 NaDIA annual reports that presented separate analysis for England and for Wales. The comparatives for 2011 and 2012 in this report will therefore differ from the figures published previously for those periods. Hospitals from Wales did not participate in the 2010 or 2019 NaDIA.

Summary data by country for England and Wales is included in the 2019 Hospital Level Analysis available from:

http://content.digital.nhs.uk/pubs/nadia2019

The section below explains the ‘all recorded data’ method used within this report. The statistical glossary at the end of the document explains the testing mechanism used within this report.

**How did we calculate the values in the audit?**

**The ‘all recorded data’ method**

The information in the National Diabetes Inpatient Audit is collected by medical and audit professionals across England and Wales using three questionnaires. We appreciate all their hard work.

The audit forms are divided into sections. When we receive audit forms most are filled in completely but some have gaps. Some sections will have an answer in some boxes but other boxes will be blank.

When we analyse the data we have to make a decision. Do we only include results for patients where every box in a section has been completed (i.e. only include complete records)? Or do we include results from all boxes that have been completed, even if there is missing information elsewhere in that section (i.e. use all the recorded data)? Both methods of analysis are valid (see the examples below).

It has been decided that the audit should be using as much of the data as possible (all recorded data). The audit report was prepared using the ‘all recorded data’ method for the first time in 2012.

For more detail or any questions please contact NaDIA@nhs.net.

Example - Insulin prescription errors:

| Insulin prescription errors | Form 1 | Form 2 | Form 3 | Form 4 | Form 5 | Form 6 | Form 7 | Form 8 | Form 9 | Form 10 | |-----------------------------|--------|--------|--------|--------|--------|--------|--------|--------|--------|---------| | Insulin not written up | Y | N | N | N | N | N | N | N | N | N | | Name of insulin incorrect (e.g. Humalog) | N | N | N | N | N | Y | N | N | N | N | | Number (dose) unclear | N | N | N | N | N | N | N | N | N | N | | Unit abbreviated to ‘u’ or written unclearly | N | N | N | N | N | N | N | N | N | N | | Insulin or prescription chart not signed by prescriber | N | N | N | Y | N | N | N | N | N | N | | Insulin not signed as given | N | N | N | N | N | N | N | N | N | Y | | Insulin given/prescribed at wrong time | N | N | N | N | N | N | N | N | N | N |

Y = did occur, N = did not occur 'Completed records method' using only forms in which every box was completed (grey columns): 2 Y in 8 forms = 25% had a prescription error.

'All recorded data method' using all completed boxes: 4 Y in 10 forms = 40% had a prescription error.

**Statistical glossary**

**Confidence intervals**

Surveys produce statistics that are estimates of the true figure for the whole population, which would only be known if the entire population was surveyed. Therefore, estimates from sample surveys are always surrounded by a confidence interval which assesses the level of uncertainty caused by only surveying a sample of service users. If 100 samples were selected, and a 95 per cent confidence interval calculated for each one, then you would expect the true value of the population to be found within 95 of those ranges.

**Calculating confidence intervals**

[ P\_{\\text{lower}} = \\frac{(2O + z^2 - z\\sqrt{z^2 + 40q})}{2(n + z^2)} ]

[ P\_{\\text{upper}} = \\frac{(2O + z^2 + z\\sqrt{z^2 + 40q})}{2(n + z^2)} ]

We have used the following calculation of a 95 per cent confidence interval (CI) for the estimate of a proportion ( p ) from a sample survey:

Where:

- ( O ) is the observed number of individuals in the sample having the specified characteristic
- ( n ) is the sample size achieved (number of useable responses);
- ( q = (1-p) ) is the proportion without the specified characteristic;
- ( z ) is the 100(1-( \\alpha/2 ))th percentile value from the Standard Normal distribution. For example for a 95% confidence interval; ( \\alpha = 0.05 ) and ( z = 1.96 ).

**Significance testing**

Significance testing of differences over time in this report compares 2019 NaDIA values with those from the previous full NaDIA collection (2017) and/or the first comparable collection (e.g. 2010). Response rates

A patient is classed as a respondent if they responded to one or more question, allowing them to express their views on areas they feel strongly about without having to complete the entire questionnaire.

8,664 inpatients responded to the patient experience element of the audit out of the total responses to the audit (15,479 patients), a response rate of 56.0 per cent.

Weighting

When conducting sample surveys it is important to consider weighting the data to allow for any survey design effects as well as potential bias caused by non-response.

The Patient Experience survey results have been weighted to reflect the differing response rates by age, ethnic group, type of admission, type and duration of diabetes, ward speciality and length of hospital stay at the time of the audit. The weights are calculated using the relative proportions of the eligible population, the Bedside Audit respondents. Information and technology for better health and care

www.digital.nhs.uk 0300 303 5678 enquiries@nhsdigital.nhs.uk @nhsdigital

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